In a Long Term Evolution-Advanced (LTE-A) system, especially in the 3rd Generation Partnership Project (3GPP) R12/R13 specification, heterogeneous networks are more and more important, and microcells are more and more densely deployed. Such increasingly dense deployment of microcells causes a User Equipment (UE) to face increasingly strong macrocell interference or microcell interference at the edge of the microcell. From the 3GPP R12 specification, a terminal is first required to support a receiving algorithm of Network Assistant Interference Cancellation and Suppression (NAICS), a typical NAICS receiver is only able to solve one strong-interference adjacent cell, and other adjacent cells are regarded as random interference.
In the existing art, the UE detects a transmit signal in the MIMO system by using a receiving signal, a channel estimation, and an interference noise covariance matrix.
Specifically, a system equation for the receiving signal is: Y=HX+N, where Y is a receiving signal vector, H is a channel matrix, X is a transmit symbol vector, and N is an interference noise vector. Here, the interference noise vector includes two parts: interference and white noise. For the NAICS system, the channel and the transmit symbol of the solved strong-interference adjacent cell are equivalently and respectively included in H and X, and interference of other cells is embodied in N.
R is defined as the interference noise covariance matrix, i.e., R=E(NNH), where R is a positive definite Hermitian matrix and H in NNH denotes a complex conjugate transpose matrix. Here, the UE may estimate a parameter value of R by various methods.
In the existing art, detection on a signal of the UE and detection on a parameter of an adjacent cell in the NAICS are generally performed by using algorithms such as Zero Forcing (ZF), Minimum Mean Square Error (MMSE), Maximum Likelihood (ML), and Reduced Maximum Likelihood (R-ML) (including Sphere decoding (SD)), and these algorithms are only suitable for the case where R=k*I, where k is a real constant coefficient and denotes white noise power, and I is a unit matrix. Details are shown in FIG. 1.
FIG. 1 is a schematic diagram illustrating an implementation process of signal detection.
As shown in FIG. 1, k, Y and H are inputted, the estimation {circumflex over (X)} of X is obtained through the algorithms such as ZF, MMSE and R-ML (including SD) in a detection unit, and a parameter estimation corresponding to the adjacent cell is obtained in the NAICS parameter blind detection.
When interference from an adjacent cell exists, R no longer satisfies the property of (k*I), and for the MMSE algorithm, MMSE-Interference Rejection Combining (MMSE-IRC) may be used for detection, while other algorithms cannot be used for effective interference rejection. Then, a whitening matrix W is required to whiten the system equation, i.e., WY=WHX+WN, and then the system equation is equivalently transformed into: Yw=Hw X+Nw, where YW=WY and HW=WH, and the whitened covariance matrix Rw is: Rw=E(NwNwH)=WRWH.
W is suitably chosen so that Rw has the form of (k*I). Examples are described below.
(a) W=R−1/2.
(b) Cholesky decomposition is performed on R−1, R−1=UH·U, where U is an upper triangular matrix, and W=U.
(c) Cholesky decomposition is performed on R, R=L·LH, where L is a lower triangular matrix, and W=L−1.
Rw may have the form of (k*I) by using any one of the above three types of W (not limited to the three types) in (a), (b), and (c). The form (c) is commonly used in the industry since the lower triangular matrix is more convenient for inversion. The form (c) is taken as an example for demonstration.
W=L−1 is substituted into a whitened covariance matrix Rw, and then Rw=WRWH=L−1RL−H=L−1L·LHL−H=I=1·I.
The demonstration shows that Rw, being whitened, has the form of (k*I), and that k is further a constant “1”, and the same results may be demonstrated for (a) and (b). Therefore, with adding only one whitening unit, the UE can use the original various detection algorithms, and details are shown in FIG. 2.
FIG. 2 is a schematic diagram illustrating an implementation process of signal detection through a whitening matrix.
As shown in FIG. 2, a whitening matrix W is obtained through a covariance matrix R, then the whitening matrix W and Y and H in a system equation are used as input of a whitening unit, a whitening calculation is performed on the Y and the H to obtain whitened YW and whitened HW, the Whitened YW and Whitened HW are Detected Through Algorithms Such as ZF, MMSE and R-ML (including SD) in a detection unit to obtain the estimation {circumflex over (X)} of X, and the parameter estimation corresponding to an adjacent cell is obtained in the NAICS parameter blind detection process.
FIG. 2 shows that k is always a fixed constant “1”, which refers to that interference noise is eventually equivalent to a white noise level of “1” no matter how large the interference noise is. When the UE is in an environment with very low interference noise, interference noise power is very low, and through whitening, Y and H are amplified by many times in value and become YW and HW. When the UE is in an environment with big interference noise, the interference noise power is large, and Y and H are reduced through whitening by many times in numerical value and become YW and HW, so that YW and HW changes greatly and the changing ranges need a large bit width for representation, and the area of the detection unit performing signal detection through ZF, MMSE and R-ML algorithms (including SD) is greatly increased. Meanwhile, a frequent change of the interference environment directly causes a large numerical range of R, so that an area in the process of calculating the whitening matrix W through the covariance matrix R is relatively large.